Query Expansion Using Wikidata and Hybrid Dense-Sparse Retrieval for Semantic Information Access

Abstract

Standard information retrieval systems often miss deeper meaning when queries are short, vague, or lack specifics. We propose a hybrid framework that pulls structured data from Wikidata and combines dense and sparse retrieval methods to improve both finding documents and generating answers. Our method first identifies key entities in a user query using a lightweight linker, then expands the query with Wikidata properties (instance of, subclass of, part of, different from). A hybrid retriever ColBERTv2 for dense late interaction plus SPLADE for sparse lexical expansion processes the expanded query. The top ten passages then feed a FLAN-T5 generator. A DeBERTa based filter prevents semantic drift and entity confusion. Tests on TREC Deep Learning Track and three BEIR subsets (NQ, HotpotQA, FiQA-2018) show that knowledge-guided expansion outperforms five strong baselines: Standard RAG, HyDE, REPLUG, Self-RAG, and SKR. On TREC DL, nDCG@10 rises from 0.689 to 0.812 (+17.9%); average BEIR nDCG@10 increases from 0.562 to 0.673. BLEU scores for generated answers reach 0.612, a 0.147 absolute gain over Standard RAG (0.465). Ablation tests confirm that adding knowledge graph data provides the largest lift (Δ nDCG@10 = +0.097). These outcomes show that fusing structured world knowledge with hybrid neural retrieval reduces vocabulary mismatch and improves both retrieval accuracy and answer faithfulness, especially for sparse or ambiguous queries.